this is the only pie chart you will see all semester.
Welcome to ANTH 6413!
Statement about the world we live in
As your instructor, I recognize that students face a variety of challenges, and that these challenges are not evenly distributed across individuals for a variety of reasons, including:
- exposure to systemic injustice
- financial situation
- housing situation
- childcare responsibilities
- mental and physical health status
- health status of family members
My goal is to be as flexible as I can be with each student so that everyone can meet the learning objectives for the course despite the difficulties we face.
Course Goals
- Gain the statistical knowledge to critically evaluate the use of statistics in the literature of bioanthro and related fields.
- Get hands-on experience implementing analyses with real world data
- Learn practical tools for doing “data-science”
Student introductions
Lets take a minute to introduce ourselves.
- What is your name and what pronouns do you use?
- Who is your advisor / what are your research interests?
- What do you want to be able to do in 14 weeks that you can’t do today? (in regards to this class)
- If you could only eat one meal / type of food for the rest of your life what would it be?
All fields are becoming more data intensive
“Data-science”
Data science is an industry buzzword and cliche
From wikipedia:
“Data science is an interdisciplinary field about processes and systems to extract knowledge or insights from data.
- In private industry, you extract insight from data to make business decisions.
- It is also a big part of what scientists do!
- Processes and systems for getting insight from data are important!
Data science pipeline
Confession: I believe tools matter, a lot
- We will spend a lot of time on the “how”
- You will become very familiar with the nuts-and-bolts of analyzing data
- Most of your grade comes from practical exercises
- I don’t expect you to be a statistician (I sure as hell am not). But I do expect you to learn how to extract insights from data
Grading
| Assignment | Percent |
|---|---|
| Weekly homework assignments - submitted on Blackboard | 40% |
| Practical Exam 1 | 12.5% |
| Practical Exam 2 | 12.5% |
| Original Analysis (due Dec 5th) | 15% |
| Critical Evaluation (due Nov 7th) | 10% |
| Attendance/Participation in class | 10% |
Remote attendance only by excused absence
Classes will be held in person, and attendance will be taken.
Classes will not be recorded.
Your primary study materials will be the lecture slides (available on course website).
Class ground-rules
- Be respectful - language matters
- Don’t make assumptions about individuals abilities or experiences
- Don’t dominate the discussion - leave room for others to speak
- Do keep it lighthearted! This is hard, but learning is fun, y’all!
Homework
There are weekly homework assignments in this class.
These are where you will really learn to do data analysis. I will walk you through the concepts in class, but you will learn by doing.
Homework assignments will be uploaded to Blackboard, and are due by the beginning of class on their due date.
Note: First homework assignment is due next week.
I am not a monster, but in order for you to learn, you need to do these in a timely fashion, because concepts build on one another fast!
Generative AI (e.g. ChatGPT)
You are free to use available tools to help complete homework. You are expected, based on the GW honor code to cite sources if you copy and paste code. This includes output from ChatGPT.
ChatGPT is an incredibly useful course for producing code, but it can’t teach you how to write code, or to understand it. If you don’t understand what you are doing on homework, you will not be able to pass the exams, which will be completed without ChatGPT.
Outcome of the course
When you leave this course, I hope you are empowered to:
- collect your data in a format that makes your future life easier
- explore patterns in your data visually and statistically
- test meaningful hypotheses using appropriate statistical tools
- communicate these results effectively using graphs and prose
Tools of the Trade
- MS Excel
- Commercial Statistics Packages (e.g., SPSS)
- R – open source and free package for statistical analysis
Tools of the Trade
- MS Excel
Commercial Statistics Packages (e.g., SPSS)- R – open source and free package for statistical analysis
Why do I use R?
- Ignorance - Because I don’t know how to use anything else
- Comprehensiveness – Learn this one tool well, and you can do (almost) ANYTHING
- Reproducibility – an R script is a detailed record of exactly what I did, from the raw data file, through data transformation, to analysis and results.
- Popularity - R is insanely popular in academia and in industry (“why hello there, marketable skills!”)
What is R?
R is an open-source language and environment for statistical computing and graphics.
open-source means that anyone can look at the code to see how it works.
What is R?
One of the top 10 most widely used programming languages in the world in 2018.
What is R?
A rapidly growing system of user contributed packages.
Course Website
Let’s check out the website then we will do the introduction to R
Intro to R
We are going to do an interactive tutorial that I wrote.
Download the folder at this link: link
Be sure you have installed the learnr package. Who needs help doing this?
We will spend about 40 minutes on the tutorial